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Lillian Zhou

2 accepted papers

2022

Enabling On-Device Training of Speech Recognition Models With Federated Dropout

ICASSP 2022accepted

Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default. This presents a challenge pertaining to the communication and computation costs associated with clients’ devices. These costs are strongly correlated with…

Cited by 0SourceScholar
2022

Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training

ICASSP 2022accepted

Transformer-based architectures have been the subject of research aimed at understanding their overparameterization and the non-uniform importance of their layers. Applying these approaches to Automatic Speech Recognition, we demonstrate that the state-of-the-art Conformer models generally have mult…

Cited by 0SourceScholar